Root responses to warming and hurricanes

The Science
This study measured root responses to an increase in soil temperature and two hurricane disturbances. We used root images to measure root production, mortality, and biomass. We also measured aboveground and soil variables to test how these responses changed with environmental variables. We found that root production and biomass decreased with warming. Further, root recovery after the hurricanes was slower in warmed plots compared to controls.

The Impact
Tropical forests are expected to be greatly affected by global warming in the next 20 years. However, it is not known how these forests will respond to this climatic change. Specifically, plant root responses to warming are not well represented in the literature. Our study provided for the first-time measurements on root responses to warming and two hurricanes in a tropical forest. Our results suggest that a decrease in root production in a warmer world and slower root recovery after a hurricane disturbance might have longer term consequences on these forests.

Summary
In hurricane-adapted forests of Puerto Rico, recovery from disturbance is critical to ecosystem function. However, human-caused temperature increases could alter recovery processes. We evaluated forest root dynamics responses to experimental warming as two consecutive hurricanes passed through the island. Although warming was halted, root measurements continued, creating a unique opportunity to evaluate legacy effects of warming on forest recovery following hurricanes. Warming prior to the hurricane disturbance suppressed root production. After the hurricanes, root standing stocks increased overall due to a change in plant composition. This increase was less in previously warmed plots suggesting that antecedent warming conditions suppressed roots’ capacity to recover following hurricane disturbance. Our results suggest tropical forest responses to disturbance may be dramatically changed as Earth warms.

Contacts (BER PM)
BER Program Manager: Daniel Stover; U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1); daniel.stover@science.doe.gov
Brian Benscoter; U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1); brian.benscoter@science.doe.gov
Principal Investigator: Daniela Yaffar, Functional Forest Ecology, University of Hamburg, danielayaffar@uni-hamburg.de
Anthony Walker, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, walkerap@ornl.gov

Funding
This research was supported as part of the Next Generation Ecosystem Experiments‐Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research.

Publications
Yaffar D, Wood, TE, Reed SC, Branoff BL, Cavaleri MA. and Norby RJ. 2021. Experimental warming and its legacy effects on root dynamics following two hurricane disturbances in a wet tropical forest. Global Change Biology. https://doi.org/10.1111/gcb.15870
Yaffar, D. 2021. Root responses to warming and hurricane disturbances in a wet tropical forest of Puerto Rico: R code and data. https://doi.org/10.15486/ngt/1582598

ForestGEO scientists assessed multiple tree-level conditions across six tropical forests to provide a ranking of importance of tree mortality risks.

The Science
The rate at which trees are dying is increasing worldwide. Yet, little is known about what kills trees in natural forests. This is particularly difficult to study in diverse tropical forests, where species vary widely in their responses to different conditions. Forest ecologists assessed trees of 1,900 species in six tropical forests to provide the first ranking of importance of mortality risk factors. Among 19 mortality risk factors evaluated, researchers found that those related to tree-level damage were the dominant risks associated with tree mortality.

The Impact
This study provides the basis to prioritize next-generation experiments on tropical tree mortality. Future research should focus on the links between damage related risks, their climatic drivers, and the physiological processes to enable mechanistic predictions of tree mortality.

Summary
Carbon losses due to tree mortality in tropical forests constitute a significant source of uncertainty in vegetation models. Yet, the relative importance of mortality risk factors remains poorly understood. In this study, researchers recorded data on a broad suite of observations of living trees and monitor their subsequent survival to provide a ranking of importance of tree mortality risk factors in tropical forests. The researchers presented a new framework for quantifying the importance of mortality risk factors and applied it to compare 19 risks on 31 203 trees (1977 species) in 14 one-year periods in six tropical forests. They found that factors related to tree-level damage such as crown loss or trunk loss were the most impactful in terms of their contribution to total mortality. Leaning, defoliation and lower elevation ranked next in importance, whereas other risks expected to be important such as those associated with lianas, stranglers, trunk deformities and trunk rot were not impactful in this study. This ranking should inform research priorities and model experiments to improve predictions of the fate of forests in global dynamic vegetation models.

Contact (BER): Daniel Stover, SC-23.1 (Daniel.Stover@science.doe.gov)
Science Contact: Daniel Zuleta, Forest Global Earth Observatory, Smithsonian Tropical Research Institute (dfzuleta@gmail.com)

Funding
This project and DZ were supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. Data collection was supported by the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Institution. 

Publications
Zuleta, D., Arellano, G., Muller-Landau, H.C., McMahon, S.M., Aguilar, S., Bunyavejchewin, S., Cárdenas, D., Chang-Yang, C.-H., Duque, A., Mitre, D., Nasardin, M., Pérez, R., Sun, I.-F., Yao, T.L. and Davies, S.J. “Individual tree damage dominates mortality risk factors across six tropical forests.” New Phytologist (2021). [DOI: 10.1111/nph.17832]

Best practices, a computer package, and hands-on tutorials to guide the suggested application of a powerful emerging technique for prediction of plant traits from hyperspectral data. 

The Science
The estimation of leaf traits, such as leaf nitrogen, from hyperspectral reflectance data enables rapid, high-throughput, non-destructive characterization of leaf function and plant phenotyping with applications in ecosystem characterization and monitoring. Partial least squares regression (PLSR) is a popular method for the prediction of a wide range of nutritional and structural leaf traits using hyperspectral data. However, there has not been a standard approach for developing and reporting these models and results. This has led to challenges in the wider application of the technique. We developed a detailed description of use of PLSR to predict leaf traits with spectra and our recommended best practices across all steps of the process, from experimental design, data collection, PLSR model building, model application and reporting of results. With the article, we also provide hands-on tutorials to assist users to in understanding the PLSR modeling and application of our best practices with their own data.

The Impact
Plant scientists require detailed and extensive information on the concentration and distribution of physiological and structural leaf properties to study vegetation responses to environmental change, monitor plant health, and facilitate the rapid screening of different plant phenotypes. Traditional approaches to measure these traits directly are expensive and logistically challenging. Here we summarize and illustrate an alternative spectroscopic approach for the rapid, accurate and non-destructive estimation of traits using remote sensing data.

Summary
Plant physiologists and ecologists regularly measure leaf functional traits, including leaf nitrogen or photosynthetic rate, across a range of leaves, plants, species, or environments. These direct measurements, while very accurate for characterizing leaf structure and function, are typically slow, expensive, and can be logistically challenging.  In addition, many ecological or phenotyping studies require a large number of samples, which can be impractical with traditional methods. On the other hand, remote sensing methods have been shown to be effective for the rapid estimation of many of key leaf traits, however the inconsistent usage of the methods have led to challenges in the wider application across the plant sciences. To address this challenge, and to help standardize the approach across studies to facilitate wider adoption, we provide a detailed summary of the spectral method of leaf trait estimation. We also provide clear examples and tutorials to illustrate how to use the approach, and we discuss a range of suggested “best-practices”. Importantly, we also highlight how the same approach can be scaled up to estimate vegetation traits across landscapes using non-contact remote sensing data.

Contact (BER): Daniel Stover, SC-23.1 (Daniel.Stover@science.doe.gov)
Science Contact: Shawn P. Serbin, Brookhaven National Laboratory, sserbin@bnl.gov (+1 631-344-3165)

Funding
This work was supported by the Next-Generation Ecosystem Experiments (NGEE Arctic and NGEE Tropics) projects that are supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory.

Publications
Burnett, A. C., J. Anderson, K. J. Davidson, K. S. Ely, J. Lamour, Q. Li, B. D. Morrison, D. Yang, A. Rogers, and S. P. Serbin. 2021. “A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression”. Journal of Experimental Botany. [DOI: https://doi.org/10.1093/jxb/erab295]

Related Links
Article URL: https://doi.org/10.1093/jxb/erab295
Companion computer code: https://zenodo.org/record/4730995
Brookhaven National Laboratory “Of Leaves and Light” article on the use of spectroscopy to estimate leaf functional traits https://www.bnl.gov/newsroom/news.php?a=213279
Open-source book chapter, “Scaling Functional Traits from Leaves to Canopies”: https://link.springer.com/chapter/10.1007/978-3-030-33157-3_3

Congratulations to Charlie Koven, our NGEE-Tropics modeling lead and a staff scientist at Lawrence Berkeley National Laboratory (LBNL), for being awarded 2021’s Piers J. Sellers Global Environmental Change Mid-Career Award from the American Geophysical Union (AGU). AGU’s Piers J. Sellers Global Environmental Change Mid-Career Award is an annual award that “recognizes outstanding contributions in research, educational, or societal impacts in the area of global environmental change, especially through interdisciplinary approaches” (from AGU’s website). Please check out the full LBNL Earth and Environmental Sciences Area (EESA)’s press release on Charlie’s recent award here. The award will be bestowed during December’s upcoming AGU Fall Meeting — don’t forget to drop by and congratulate Charlie if you will be in attendance!

Image courtesy of Christina Procopiou’s EESA press release on Charlie Koven’s receipt of the Piers J. Sellers Global Environmental Change Mid-Career Award.

A team assessed how factors affect forest disturbance intensity across multiple regions and examined the difficulty to build a general cyclone impact model.

The Science
Cyclones have huge impact on our planet earth. After cyclone passed, some trees were fallen while some trees just lost some leaves. Why is that? Scientists use satellite images and study what factors contribute to the different impact on forests brought by hurricanes. Scientists found a 40 m/s wind speed threshold that affect the cyclones impact, but little consistency can be found on other variables among the studies of different cyclones. Each cyclone interacted with the landscape in a unique way. In addition, they discussed the difficulties to build a model which can predict where the next damage region would be due to upcoming cyclone.

The Impact
Climate change enhances cyclone-related rainfall, which worse the impact of cyclone on forests. Therefore, it is important to understand how the forested in different regions were affected by past cyclones and get better insights to prepare for future cyclones. This study reveals the links between remote sensing images derived forest disturbance intensity and the factors, including wind and rainfall, forest structure, terrain features, and soil properties at the landscape scale. The authors are the first to discuss the possibility to build a machine learning model to predict the impact of an unseen hurricane on forests.

Summary
This study addressed the importance of climate variables, terrain features, and forest properties in predicting tree damage caused by cyclones. Wind, elevation, and pre-disturbance vegetation condition are strong predictors. Machine learning technologies were used to build cyclone impact models. As machine learning models become more popular in earth science, this study showed that them still had limitations in cyclone effects prediction. The models worked well on hold out test data, but they had weak predictability on unseen cyclones. The authors believed that more finer scale data can be helpful to build local models work with similar ecosystems and landscapes, but the complexities of cyclone effects coupled with landscapes, soils, states of affected systems, climate change leads us to question the existence of an omnipotent cyclone impact model that works for the globe.

Contact (BER): Daniel Stover, SC-23.1 (Daniel.Stover@science.doe.gov)
Science Contact: Yanlei Feng, University of California, Berkeley (ylfeng@berkeley.edu)
Jeffrey Q. Chambers, Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, USA (jchambers@lbl.gov)

Funding
This research was supported as part of the Next Generation Ecosystem Experiments-Tropics (NGEE), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231.

Publications
Feng, Y., Negrón-Juárez, R., Chambers, J. Q. (2021) Multi-cyclone analysis and machine learning model implications of cyclone effects on forests, International Journal of Applied Earth Observation and Geoinformation, Volume 103, https://doi.org/10.1016/j.jag.2021.102528.

Related Links
Disturbance intensity map of hurricane disturbance. Katrina, Rita, Yasi, María have been validated with field observations and high-resolution airborne remote sensing images. UI Interface: https://ylfeng.users.earthengine.app/view/jagfengetal
Supplementary data to this article can be found online at https://doi. org/10.1016/j.jag.2021.102528.